title: Self-assessment for optic disc segmentation.
abstract: Optic disc segmentation from retinal fundus image is a fundamental but important step in many applications such as automated glaucoma diagnosis. Very often, one method might work well on many images but fail on some other images and it is difficult to have a single method or model to cover all scenarios. Therefore, it is important to combine results from several methods to minimize the risk of failure. For this purpose, this paper computes confidence scores for three methods and combine their results for an optimal one. The experimental results show that the combined result from three methods is better than the results by any individual method. It reduces the mean overlapping error by 7.4% relatively compared with best individual method. Simultaneously, the number of failed cases with large overlapping errors is also greatly reduced. This is important to enhance the clinical deployment of the automated disc segmentation.
authors.forename: Jun
authors.initials: J
authors.lastname: Cheng
authors.forename: Jiang
authors.initials: J
authors.lastname: Liu
authors.forename: Fengshou
authors.initials: F
authors.lastname: Yin
authors.forename: Beng-Hai
authors.initials: BH
authors.lastname: Lee
authors.forename: Damon Wing Kee
authors.initials: DW
authors.lastname: Wong
authors.forename: Tin
authors.initials: T
authors.lastname: Aung
authors.forename: Ching-Yu
authors.initials: CY
authors.lastname: Cheng
authors.forename: Tien Yin
authors.initials: TY
authors.lastname: Wong
mesh_terms: D000465:Algorithms; D016001:Confidence Intervals; D005901:Glaucoma / Q000175:diagnosis; D006801:Humans; D007091:Image Processing, Computer-Assisted; D009898:Optic Disk / Q000033:anatomy & histology*